Ontology-Based Clustering in a Peer Data Management System

Abstract

Peer Data Management Systems (PDMSs) are advanced P2P applications in which each peer represents an autonomous data source making available an exported schema to be shared with other peers. Query answering in PDMSs can be improved if peers are efficiently disposed in the overlay network according to the similarity of their content. The set of peers can be partitioned into clusters, so as the semantic similarity among the peers participating into the same cluster is maximal. The creation and maintenance of clusters is a challenging problem in the current stage of development of PDMSs. This work proposes an incremental peer clustering process. The authors present a PDMS architecture designed to facilitate the connection of new peers according to their exported schema described by an ontology. The authors propose a clustering process and the underlying algorithm. The authors present and discuss some experimental results on peer clustering using the approach.